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Typical sensor network scenarios assume that sensor nodes are densely deployed in the monitored area Greedy routing algorithms applied on uniformly distributed and densely deployed network nodes perform close to the shortest-path algorithm and are thus the rst choice for such a scenario However, even for densely deployed network nodes a recovery strategy may still be necessary, since greedy forwarding might get stuck at convex network boundaries or at network voids resulting from an inhomogeneous node distribution Such an inhomogenity may be due to the physical properties of the monitored surface (eg, a lake inside the monitored area where sensor nodes cannot be deployed) Frey and Gorgen [23] observed that such an inhomogeneous node distribution can have a signi cant impact on the recovery strategy being applied.

Test with screen resolution set to: 640 480 800 600 1024 768

Simulation experiments show that performance of face routing and the internal nodes concept may even degrade when node density is increased in a network scenario with an inhomogeneous node distribution On the other hand, face recovery in combination with the shortcut procedure is almost unaffected by such an inhomogenity However, the shortcut procedure has an increased message complexity compared to face routing or the internal nodes concept, since information about all 2-hop neighbors is required Frey and Gorgen [23] described the geographic cluster routing (GCR) algorithm, which is based on the concept of GFG and assumes that the network is modeled as a unit-disk graph in the 2D Euclidean space as well Routing in GCR is not performed on a per-node basis, but packets are forwarded along the edges of adjacent geographical clusters.

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In order to de ne geographical clusters, the plane is partitioned by an in nite mesh of regular hexagons (see Fig 125), while each hexagon de nes one cluster Two geographical clusters C1 and C2 are denoted as adjacent, if there are at least two connected nodes with one located in C1 and the other located in C2 The graph resulting from adjacent clusters is not necessarily planar, thus, before face routing can be applied, a planar subgraph has to be extracted in advance This is obtained by a variant of the localized planar graph construction applied by GFG However, the method used by GCR may produce a disconnected subgraph even when the original graph is connected Thus, in contrast with GFG and its variants, GCR cannot guarantee delivery, even if there is a path from source to destination.

However, simulation results reveal that the delivery rate quickly tends to 100% when network degree is increased In particular, in densely deployed networks.

In the sections that follow, I discuss the various types of files you can and cannot use with Excel.

When two box-plots are compared, the analyst is undertaking the graphical analog of the t test. Displays with additional boxes, as shown here, are analogous to the analysis of variance: Group-level measures of central tendency are displayed relative to the amount of within-group variability in the data. Although the box-plots are very useful and informative in their current state, working in the exploratory mode raises additional issues. First, how might we be fooled by these displays The answer to this is that there are times when the

Rumsey, J., 337 Runyon, M. K., 340 Rupert, P. A., 410 Rush, A. J., 464 Russell, O., 338, 339 Russell, R. K., 377, 380, 831 Russell, R. L., 308 Russo, N. F., 378 Ryan, R. M., 130 Sachs, J. S., 317 Sackheim, H. A., 151 Sadler, G., 326, 327 Sadock, B. J., 151 Saeman, H., 445, 446 St. Lawrence, J. S., 331 Salkovskis, P. M., 463, 472 Saltz, R. F., 293 Salvio, M., 309 Sammons, M. T., 449, 451, 452 Sanchez, P. N., 253 Sanders, M. J., 341 Sanderson, C. A., 444 Sanderson, W. C., 15, 103, 105, 122, 304, 308, 314, 455, 456 Sandhu, D. S., 153 Sandler, J., 341 Sandoval, J., 233 Sanford, N., 302 Santoro, S. M., 16, 17, 62, 69, 71, 147, 250, 275, 277, 280, 377, 441, 453, 495 Sapolsky, R. M., 330 Sasao, T., 329 Satir, V., 65 Sattler, J. M., 222, 232, 233 Saunders, A. M., 573 Saura, J., 347 Savro, M., 166, 168 Sawyer, J. D., 315 Sayette, M. A., 6, 16, 17, 19, 20, 66, 67, 69, 266, 436, 453, 458, 478, 479, 482, 483, 484, 485, 486, 490, 496 Scanlan, J. M., 158 Schacter, D. L., 316 Schaefer, C. E., 474 Schaefer, V., 434 Schein, E. H., 367, 374 Scherg, H., 170 Scheufele, P. M., 330

The physical layer has four important characteristics:

1 1 1 0

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